A decision support system to improve the efficiency of resource allocation in healthcare management

Aktaş, Emel; Ülengin, Füsun; Şahın, Şule

doi:10.1016/j.seps.2005.10.008

Cited by 62 publications

(28 citation statements)

References 29 publications

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“…In the BCN literature, this process is referred to as discretisation (Häger, & Andersen, 2010). scale, we divided the related range into three intervals that corresponded to the three states by checking the break points in data as is explained in Aktas et al (2007).…”

Section: Bcn Model Of the Automotive Industrymentioning

confidence: 99%

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Ülengin

Önsel

Aktaş

et al. 2014

European Journal of Operational Research

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Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum's competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey.

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Section: Bcn Model Of the Automotive Industrymentioning

confidence: 99%

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Ülengin

Önsel

Aktaş

et al. 2014

European Journal of Operational Research

Self Cite

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“…They used the analytic hierarchy process to identify and prioritize the goal levels. Aktas et al (2007) proposed a management-oriented decision support model to assist health system managers in improving the efficiency. They identified key variables of system efficiency and employed a Bayesian belief network to model the causal relationships.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy decision making in health systems: a resource allocation model

Ekin¹,

Kocadağlı

Bastian

et al. 2016

EURO Journal on Decision Processes

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“…Kwak et al [36] used the analytic hierarchy process to identify and prioritize the goal levels within a similar multi-criteria mathematical program. Aktas et al [1] proposed a management-oriented decision support model which employed a Bayesian belief network that models the causality among the key variables of system efficiency. Fulton et al [21] used DEA and SFA to identify the cost drivers for performance-based resource allocation, which later are used as part of the direct decision support methods for the MHS.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

Bastian

Ekin²,

Kang

et al. 2016

Health Care Manag Sci

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The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

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A decision support system to improve the efficiency of resource allocation in healthcare management

Cited by 62 publications

References 29 publications

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Fuzzy decision making in health systems: a resource allocation model

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

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